Geological structure physical simulation experiment optimization scheme selection method

Abstract

The invention discloses a geological structure physical simulation experiment optimization scheme selection method. The method comprises the following steps: determining the purpose and requirements of an experiment; determining a geologic model, setting experimental devices and models, selecting experimental materials and setting experimental conditions; condition parameters influencing the experiment result are determined as experiment factors, and the corresponding experiment level is determined; constructing a factor level table; constructing a uniform design table design test, performing uniformity measurement for the mixing level test, and designing and constructing an optimal uniform design table; homing each test parameter, and translating the test parameters into an optimal design scheme table; carrying out a simulation experiment based on the optimal experiment design scheme table, and carrying out quantitative evaluation on an experiment result; analyzing the experiment result, constructing a proper mathematical model, checking the significance of the experiment factor, and determining the level parameter of the optimal experiment factor of the experiment; and performing experimental verification and process analysis according to the optimal test factor level parameter. The experiment frequency can be reduced, the experiment process is simplified, and the experiment efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geological structure physical simulation experiment, and more particularly, to a geological structure physical simulation experiment optimization scheme selection method. BACKGROUND

[0002] Geological structure physical simulation is an experimental technique that can reproduce the deformation process of geological structure under laboratory conditions, and quantitatively analyze the deformation results and the geological structure mechanism through digital recording, image playback and image processing. Through the structure physical simulation experiment, the formation, development and evolution process of various structural forms can be intuitively analyzed, the formation mechanism can be discussed, the dynamics model can be established, the details of the structure formation can be understood, and the possible structure can be predicted. It can also be used to provide a structural interpretation template for seismic profiles and infer deep structural characteristics of poor seismic quality.

[0003] The structure physical simulation experiment has made great progress in experimental theory, technology, materials and methods, but still has some deficiencies, which are controlled by various factors including the design of the pre-experimental model, the characteristics of the deformed material and the dynamics mechanism, and specifically represented as: (1) selection of experimental materials; (2) setting of various experimental parameters and conditions; (3) determination of the optimal experimental scheme. The first two problems can be solved before the optimal experimental scheme is obtained by referring to the research results template of predecessors, field geological survey and seismic profile interpretation, and systematically studying the geometric, kinematic and dynamic characteristics of the research object. However, the last problem is still in the exploratory, tedious and approximate experimental stage, and there is blindness and randomness. No effective optimal scheme selection work is performed, and the conventional experimental method often uses the control variable method to analyze the single factor, ignoring the interaction, resulting in that the experimental results cannot achieve the expected purpose. SUMMARY

[0004] The purpose of the present application is to provide a geological structure physical simulation experiment optimization scheme selection method, which solves the problem of blindness and randomness in the exploratory, tedious and approximate experimental stage of the geological structure physical simulation experiment in the prior art, and achieves the purposes of reducing the number of experiments, simplifying the experimental process and improving the experimental efficiency.

[0005] To achieve the above purpose, the present application provides a geological structure physical simulation experiment optimization scheme selection method, comprising:

[0006] S1: finding out the geological background, development situation, geometric, kinematic and dynamic characteristics of the simulation object in the geological structure physical simulation experiment, and determining the purpose and requirements of the structure physical simulation experiment;

[0007] S2: According to the purpose of the geological structure physical simulation experiment, the geological model is determined, the experimental device and model are set, the experimental material is selected, and the experimental condition is set;

[0008] S3: Determine the condition parameters affecting the results of the structure physical simulation experiment as test factors, and determine the test levels corresponding to each test factor;

[0009] S4: The determined test factors and corresponding test levels are specifically designed, and a factor level table is constructed;

[0010] S5: According to the factor level table, an even design table is constructed to design the test, the uniformity is measured by using the deviation D for the mixed level test, and the optimal even design table is designed and constructed;

[0011] S6: According to the optimal even design table, each test parameter is returned to the position, and the optimal design scheme table of the even test design is translated;

[0012] S7: Based on the optimal test design scheme table, the corresponding geological structure physical simulation experiment is carried out, the discrete Fréchet distance and the fractal dimension are used as indexes for the experimental results, and the multi-scale space target similarity measurement method is used for quantitative evaluation;

[0013] S8: The quantitative evaluation results of the experiment are analyzed, a suitable mathematical model is constructed, and the significance of the test factors is tested by using the multiple regression method combined with F test and correlation coefficient (r) test, and the optimal test factor level parameter of the geological structure physical simulation experiment is determined;

[0014] S9: According to the optimal test factor level parameter, the geological structure physical simulation experiment verification and process analysis are carried out.

[0015] Optionally, the S1 specifically comprises:

[0016] The geological structure background of the target work area is found out, the geometric characteristics, kinematic process and dynamic background of the development of the geological structure in the work area are determined, the tectonic stress background of different geological periods is determined as extension, compression or strike slip, and the purpose and requirements of the tectonic physical simulation experiment are determined.

[0017] Optionally, the S2 specifically comprises:

[0018] According to the purpose of the geological structure physical simulation experiment, the geological model to be subjected to the tectonic physical simulation experiment is determined, wherein the geological model includes a geological section interpreted according to a seismic section;

[0019] A fully automated geological structure physical simulation experimental sandbox system was used as the experimental device. The specifications of the experimental model sandbox were set according to the size of the target work area and the similarity ratio. The driving units at both ends of the experimental model sandbox were set to tension or compression, fixed or movable, unidirectional or bidirectional according to the structural stress background and stress conditions of the work area. The boundary conditions of the experimental model sandbox were set according to the positional relationship of the actual geological phenomena in the work area.

[0020] Based on the rock mechanical properties of the study area, the most similar experimental materials were selected, and based on the actual structural deformation conditions, the external factors that most closely resembled the real geological phenomena were selected for the experiment. The external factors included: the external force application rate, the force application direction, the compression or tension distance, and the stratum thickness.

[0021] Optionally, S5 specifically includes:

[0022] Using the aforementioned factor level table, a uniform design table is constructed to design experiments. For mixed-level experiments, the uniformity is measured using the deviation D calculation formula to obtain the deviation D value of the mixed-level design table.

[0023] The deviation D value of the mixed-level design table is compared with the deviation D value of the uniform design table, and the optimal uniform design table is constructed based on the design table with the smallest deviation D value.

[0024] Optionally, the deviation D is calculated as follows:

[0025]

[0026] Where x1, x2, ..., x n For the vector set C m n test points are uniformly distributed in the middle, x = (x1, x2, ..., xn). m )∈C m Let n be any vector x Let x1, x2, ..., x m The number of points that fall within the range [0, x], n x / n represents the proportion of points falling within the rectangle [0, x], ν(x) = x1, x2, ..., x m Let be the volume of the rectangle [0, x], D denote the deviation, and sup denote the supremum of the set.

[0027] Optionally, S7 specifically includes:

[0028] Conduct corresponding geological structure physical simulation experiments according to the optimal design scheme table;

[0029] The experimental results are quantitatively evaluated, and a multi-scale spatial line target similarity measurement method of spatial relation similarity theory is selected as an evaluation index, and a discrete Fréchet distance between line targets is taken as a measurement index of position similarity;

[0030] A fractal dimension of the line target is taken as shape similarity of the similarity measurement, and a box-counting dimension in the fractal dimension is taken as a measurement index of shape similarity;

[0031] The weight of the position similarity and the shape similarity is balanced, and a total similarity is calculated as a final evaluation index of the experiment.

[0032] Optionally, a calculation formula of the discrete Fréchet distance is as follows:

[0033]

[0034] Wherein, d E (L 1，n , L 2，m ) is a Euclidean distance between two points L 1，n and L 2，m , from (<L 1，n >, <L 2，m >), <L 1，1 , L 1，2 , …, L 1，n-1 > and <L 2，1 , L 2，2 , …, L 2，m-1 > can be regarded as line strings, the discrete Fréchet distance between <L 1，1 , L 1，2 , …, L 1，n-1 > and <L 2，1 , L 2，2 , …, L 2，m-1 > is calculated recursively, and the calculation is terminated when the line string is finally reduced to the starting point <L 1，1 >, <L 2，1 >), and the discrete Fréchet distance D Fd (<L 1，n >, <L 2，m >) = d E (L 1，1 , L 2，1 ) at this time.

[0035] A calculation formula of the position similarity is as follows:

[0036]

[0037] Wherein, A and B are two spatial line targets to be compared, and D Fd(A, B) is the discrete Fréchet distance between A and B, U is the maximum distance between any two points in A and B, Sim d (A, B) represents the position similarity between A and B.

[0038] Optionally, the box dimension calculation formula is:

[0039]

[0040] wherein r is the side length of the square grid covering the geometric trace, Nr is the number of non-empty grids intersecting the geometric trace, and D is the box dimension value.

[0041] The shape similarity calculation formula is:

[0042]

[0043] wherein A and B are two spatial line objects to be compared, SimFrc(A, B) is the shape similarity of spatial line objects A and B, Dim(A) is the box dimension of A, Dim(B) is the box dimension of B, Sim Frc (A, B) represents the shape similarity between A and B.

[0044] Optionally, the total similarity calculation formula is:

[0045] Sim Tot (A, B) = αSim d (A, B) + βSim Frc (A, B)

[0046] wherein Sim Tot (A, B) is the overall similarity of spatial line objects A and B, and α and β represent the weights of the position relationship and shape relationship similarity, respectively.

[0047] Optionally, the S8 specifically comprises:

[0048] By analyzing the evaluation results of the physical simulation experiment of the geological structure, the linear relationship and the nonlinear relationship are determined, a corresponding mathematical model is constructed to represent the relationship between the factors and the evaluation results, and thus the optimal test factor level parameter of the physical simulation experiment of the geological structure is determined.

[0049] The F-test calculation formula is:

[0050]

[0051] wherein i is the serial number of the test factor, n represents the number of test levels, yi n represents the test score index of the ith test factor at the corresponding level, Sum of test score indicators of the i-th test factor at the corresponding level.

[0052] The present application has the advantages that:

[0053] The present application applies the uniform test design method to the optimization scheme selection of the geological structure physical simulation experiment, reduces the number of experiments through the optimization algorithm, simplifies the experimental process, uses the discrete Fréchet distance and the fractal dimension as indexes for the experimental results, and uses the multi-scale space target similarity measurement method for quantitative evaluation; the optimal parameters and the level combination can be quickly and accurately determined, the efficiency of the structure physical simulation experiment is improved on the premise of ensuring the experimental effect, the blindness and randomness of the experiment are effectively avoided, the experiment has a significant advantage for the experiment with a large number of levels, and the economic demand and the technical demand are also considered, and the present application has certain innovative significance and application prospect in the field of searching for the optimal experimental scheme of the geological structure physical simulation experiment.

[0054] The system of the present application has other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent detailed description incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0055] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of exemplary embodiments of the present application taken in conjunction with the accompanying drawings, in which like reference characters refer to the like parts throughout the figures, and in which:

[0056] Figure 1 A step flow chart of a geological structure physical simulation experiment optimization scheme selection method provided by the present application;

[0057] Figure 2 A geological comprehensive map of the western part of the southern margin of the Junggar Basin provided by the present application;

[0058] Figure 3 A schematic diagram of a certain profile section of the western part of the southern margin of the Junggar Basin provided by the present application as a geological model;

[0059] Figure 4a And Figure 4b The top view and the side view of the structure physical simulation experiment device corresponding to the geological model of the target work area provided by the present application;

[0060] Figures 5a-5d The total similarity and the scatter plot distribution of each different factor provided by the present application;

[0061] Figure 6a AndFigure 6b respectively total similarity and extrusion distance, formation thickness function relationship diagram provided by the embodiment of the application;

[0062] Figure 7 The optimal experimental scheme geological structure physical simulation experiment result schematic view provided by the embodiment of the application. DETAILED DESCRIPTION

[0063] The application will be described in more detail with reference to the drawings. Although the preferred embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the application is more thorough and complete, and the scope of the application is fully conveyed to those skilled in the art.

[0064] As Figure 1 shown, according to the geological structure physical simulation experiment optimization scheme selection method of the application, comprising:

[0065] S1: finding out the geological background, development situation, geometric, kinematic and dynamic characteristics of the simulation object in the geological structure physical simulation experiment, and clarifying the purpose and requirements of the structure physical simulation experiment;

[0066] In one example, this step specifically includes: finding out the geological structure background of the target work area, clarifying the geometric characteristics, kinematic process and dynamic background of the geological structure development in the work area, determining that the tectonic stress background in different geological periods is extension, compression or strike-slip, and clarifying the purpose and requirements of the structure physical simulation experiment.

[0067] S2: according to the purpose of the geological structure physical simulation experiment, clarifying the geological model, setting the experimental device and model, selecting the experimental material and setting the experimental conditions;

[0068] In one example, this step specifically includes:

[0069] S201: according to the purpose of the geological structure physical simulation experiment, clarifying the geological model to be subjected to the structure physical simulation experiment, wherein the geological model includes a geological section interpreted from a seismic section;

[0070] S202: using a full-automatic geological structure physical simulation experiment sand box system as the experimental device, setting the specifications of the experimental model sand box according to the size of the target work area according to the similarity ratio, setting the driving units at both ends of the experimental model sand box to corresponding tension or compression, fixed or movable, one-way or two-way according to the tectonic stress background and stress condition of the work area, and setting the boundary conditions of the experimental model sand box according to the positional relationship of the actual geological phenomena of the work area;

[0071] S203: According to the rock mechanics properties of the study area, the most similar experimental material is selected, and according to the actual tectonic deformation conditions, the external factors closest to the real geological phenomenon are selected for the experiment, including: external force speed, force direction, extrusion or tension distance and stratum thickness.

[0072] Specifically, this step determines the geological model to be subjected to tectonic physical simulation experiment according to the purpose of the tectonic physical simulation experiment, wherein the geological model can be a geological section interpreted from a seismic section, and the experimental device and model are set. The experimental device adopts a full-automatic tectonic physical simulation experiment sand box system, and the experimental model sand box is set according to the size of the target work area according to the similarity ratio; the driving units at both ends of the experimental sand box are set to corresponding tension or extrusion, fixed or movable, one-way or two-way according to the tectonic stress background and stress condition of the work area; the boundary condition setting of the experimental sand box device is set according to the positional relationship of the actual geological phenomenon of the work area. The experimental material selection and specific experimental condition setting, wherein the experimental material selection needs to select the most similar experimental material according to the rock mechanics properties of the study area; the specific experimental condition setting needs to select the external force speed, force direction, extrusion (tension) distance and stratum thickness closest to the real geological phenomenon as the external factors affecting the tectonic deformation for the experiment according to the actual tectonic deformation conditions.

[0073] S3: Determine the condition parameters affecting the results of the tectonic physical simulation experiment as test factors, and determine the test levels corresponding to each test factor;

[0074] S4: Specifically design the determined test factors and corresponding test levels, and construct a factor level table;

[0075] S5: According to the factor level table, construct a uniform design table to design the test, and use deviation D to measure the uniformity for the mixed level test, and design and construct the optimal uniform design table;

[0076] In one example, this step specifically includes:

[0077] S501: Construct a uniform design table to design the test using the factor level table, and use the deviation D calculation formula to measure the uniformity for the mixed level test, and obtain the deviation D value of the mixed level design table;

[0078] The deviation D calculation formula is:

[0079]

[0080] Wherein, x1, x2, …, x n is a vector set C m n evenly dispersed test points, x = (x1, x2, …, x m ) ∈ Cm For any vector, n x is x1, x2, …, x m The number of points falling in [0, x], n x / n represents the proportion of points falling in the rectangle [0, x], v(x) = x1, x2, …, x m The volume of the rectangle [0, x], D represents the deviation, and sup represents the upper bound of the set.

[0081] S502: Compare the deviation D value of the mixed level design table with the deviation D value of the uniform design table, and construct the optimal uniform design table according to the design table with the smallest deviation D value. The smaller the value of the deviation D, the better the uniformity.

[0082] S6: According to the optimal uniform design table, the test parameters are returned to the position, and the optimal design scheme table of the uniform test design is translated;

[0083] S7: Based on the optimal test design scheme table, corresponding physical simulation experiments of geological structure are carried out, and discrete Fréchet distance and fractal dimension are used as indexes for experimental results, and multi-scale space target similarity measurement method is used for quantitative evaluation;

[0084] In one example, the step specifically includes:

[0085] S701: According to the optimal design scheme table, corresponding physical simulation experiments of geological structure are carried out;

[0086] S702: Quantitative evaluation is carried out on the experimental results, and the multi-scale space line target similarity measurement method of the spatial relationship similarity theory is selected as the evaluation index, and the discrete Fréchet distance between line targets is used as the measurement index of position similarity;

[0087] Wherein, the calculation formula of discrete Fréchet distance is:

[0088]

[0089] Wherein, d E (L 1，n , L 2，m ) is the Euclidean distance between two points L 1，n and L 2，m , starting from (<L 1，n >, <L 2，m >), <L 1，1 , L 1，2 , …, L 1，n-1 > and <L 2，1 , L 2，2 , …, L 2，m-1 > can be regarded as line strings, and <L 1，1 , L1，2 ,..., L 1，n-1 > and <L 2，1 , L 2，2 ,..., L 2，m-1 > the discrete Fréchet distance between the line strings is computed, and when the line strings finally reduce to the starting point <L 1，1 > <L 2，1 > the computation is terminated, at which time the discrete Fréchet distance D Fd (<L 1，n >, <L 2，m >) = d E (L 1，1 , L 2，1 );

[0090] The formula for calculating the position similarity is:

[0091]

[0092] wherein A and B are two spatial line targets to be compared, D Fd (A, B) is the discrete Fréchet distance between A and B, U is the maximum distance between any two points in the targets A and B, and Sim d (A, B) represents the position similarity between A and B.

[0093] S703: The shape similarity with the line target fractal dimension as the similarity measure, wherein the box-counting dimension in the fractal dimension is used as the measure index of the shape similarity;

[0094] wherein the formula for calculating the box-counting dimension is:

[0095]

[0096] wherein r is the side length of the square grid covering the geometric trace, Nr is the number of non-empty grids intersecting the geometric trace, and D is the box-counting dimension value.

[0097] The formula for calculating the shape similarity is:

[0098]

[0099] wherein A and B are two spatial line targets to be compared, SimFrc(A, B) is the shape similarity between the spatial line targets A and B, Dim(A) is the box-counting dimension of A, Dim(B) is the box-counting dimension of B, and Sim Frc (A, B) represents the shape similarity between A and B.

[0100] S704: The total similarity is calculated as the final evaluation index of the experiment by balancing the weights of the position similarity and the shape similarity.

[0101] Wherein, the total similarity degree calculation formula is:

[0102] Sim Tot (A,B)=αSim d (A,B)+βSim Frc (A,B)

[0103] Wherein, Sim Tot (A,B) is the total similarity degree of the spatial line targets A and B, and alpha and beta respectively represent the weight of the position relationship and the shape relationship similarity, and preferably, alpha and beta take the same weight 1 / 2.

[0104] S8: analyzing the quantitative evaluation results of the experiment, constructing a suitable mathematical model, and using the multiple regression method combined with F test and correlation coefficient (r) test to test the significance of the experimental factors, and determining the optimal experimental factor level parameters of the geological structure physical simulation experiment;

[0105] This step specifically includes: analyzing the evaluation results of the geological structure physical simulation experiment, determining the linear relationship and the nonlinear relationship, constructing the corresponding mathematical model to represent the relationship between the factors and the evaluation results, and thus determining the optimal experimental factor level parameters of the geological structure physical simulation experiment;

[0106] Wherein, the F test calculation formula is:

[0107]

[0108] Wherein, i is the serial number of the experimental factor, n represents the number of test levels, yi n represents the test score index of the ith experimental factor at the corresponding level, represents the summation result of the test score index of the ith experimental factor at the corresponding level.

[0109] S9: verifying and process analyzing the geological structure physical simulation experiment according to the optimal experimental factor level parameters.

[0110] The application will be further explained and described below through a specific embodiment.

[0111] Embodiment

[0112] The geological structure physical simulation experiment optimization scheme selection method provided in this embodiment can include steps S1 to S9.

[0113] In step S1, the geological background, development situation, and geometric, kinematic and dynamic characteristics of the simulation object of the geological structure physical simulation experiment are ascertained, and the purpose and requirements of the structure physical simulation experiment are determined.

[0114] In this embodiment, the western piedmont fault-fold belt of the southern margin of the Junggar Basin is taken as the research object. The southern margin of the Junggar Basin is located in the southern part of the Junggar Basin, referring to the narrow belt in the piedmont of the northern Tianshan Mountains, including the piedmont thrust-fold structural belt and the Changji sag. It starts from the Fukang fault zone in the east and reaches the Sikeshu sag in the west, with a length of about 500 km from east to west and a width of about 40-60 km from south to north, with an area of 2.1 x 10 4 Km 2 . The southern margin of the basin is adjacent to the northern Tianshan Mountains. Since the Late Paleozoic, it has experienced multiple tectonic deformation superimpositions. Especially since the Cenozoic, the Tianshan Mountains have been uplifted, which has made the southern margin of the Junggar Basin area suffer from strong tectonic extrusion. The average crustal shortening rate is about 6 mm / a. In terms of present structure, it has the typical characteristics of "east-west segmentation and south-north zonation". Roughly taking Urumqi (88°E) as the boundary, the southern margin of the Junggar Basin can be divided into two parts: the western part of the southern margin and the eastern part of the southern margin (as shown in Figure 2 , in which ① Kalazha anticline; ② Aektun anticline; ③ Changji anticline; ④ Qigou anticline; ⑤ Qingshuihe anticline; ⑥ Nanmanasi anticline; ⑦ Nanchijiaha anticline; ⑧ Tuosai anticline; ⑨ Hutubi anticline; ⑩ Huxi anticline; Tuoguluo anticline; Manasi anticline; Huoerguos anticline; Nanchijiaha anticline; Dushanzi anticline).

[0115] Among them, the western structural belt of the southern margin of the Junggar Basin is located on the north side of the Tianshan Mountains, with a length of about 300 km and a width of 30-50 km. Since the Yanshanian period, especially during the Himalayan period, under the extrusion stress from the Tianshan Mountains, the Mesozoic and Cenozoic strata were bent along the coal seam of the Lower Jurassic Badaowan Formation (J1b) and the Paleogene Nanchijiahahe Formation (E 2-3 a) High-plasticity mudstone layers are bent to form anticlines due to the occurrence of slippage. Due to the continuous extrusion, three rows of fault-fold belts composed of multiple anticline belts and a series of thrust faults are developed.

[0116] The first row of fold belts consists of basement-involved folds, formed during the Yanshanian period, including the Tuositai anticline, Nan'anjihai anticline, Nan'manas anticline, Qingshuihe anticline, Qigu anticline, Changji anticline, Aktun anticline, and Kalaza anticline. These belts primarily develop near-east-west trending piedmont thrust tectonic zones, reflecting strong compressional deformation characteristics. The second row of fold belts commonly features a superposition of basement-involved folds and caprock detachment folds, formed during the Himalayan period. These belts mainly include the Khorgos anticline, Manas anticline, and Tugulu anticline, and develop the Hormatu detachment fault and related secondary faults such as the Khorgos fault, Tugulu fault, and Manas fault. The third row of fold belts consists of caprock detachment folds, including the Dushanzi anticline, Anjihai anticline, and Hutubi anticline. The anticlines in this tectonic zone are basically east-west trending long-axis anticlines, with steep northern limbs and gentle southern limbs, and the anticline axis is eroded. These fault-fold zones exhibit a northward-convex arc shape in plan view, and are roughly equidistant from each other, parallel to the distribution of the thrust faults at the piedmont of the northern Tianshan Mountains (e.g., Figure 2 (As shown).

[0117] In this embodiment, the purpose of the tectonic physical simulation experiment is to clarify the tectonic deformation characteristics and formation mechanism of the western piedmont fault-fold belt on the southern margin of the Junggar Basin.

[0118] In step S2, based on the purpose of the geological structure physical simulation experiment, the geological model is defined, and the experimental apparatus and model are set up, experimental materials are selected, and specific experimental conditions are set.

[0119] like Figure 3 As shown in this embodiment, the AA' survey line profile in the western part of the southern margin of the Junggar Basin is used as a geological model. From the profile, the anticline in the study area is tightly closed and the syncline is open, forming a barrier-type fold development feature.

[0120] In this embodiment, based on similarity ratio and operability, the experimental sandbox dimensions are set to 50cm (length) × 25cm (width) × 30cm (height), and the length similarity ratio of the experimental model is 1.67 × 10⁻⁶. -6 .

[0121] like Figure 4a and Figure 4b As shown, the experimental sandbox has drive units (drive motors) on the left and right sides, which are tension and compression models driven by electric cylinders; it can be tensioned and compressed simultaneously or fixed on one side and tensioned and compressed on one side; the front and back sides are thick tempered glass plates, which are convenient for taking pictures and recording.

[0122] During the experiment, the electric cylinder on one side was deactivated, one end was set as a fixed baffle, and the other end was a movable baffle. The moving speed was precisely controlled by a computer, and unidirectional positive pressure was used to simulate the phenomenon of unidirectional compression of strata in nature. The experimental model is as follows: Figure 3 As shown (the initial experimental setting is 2MPa for the pressure protection).

[0123] In selecting experimental materials, the most similar experimental materials need to be chosen based on the rock mechanical properties of the study area. The strata developed above the basement of the study area are, from bottom to top, Permian (P), Triassic (T), Jurassic (J), Cretaceous (K), Paleogene (E), Neogene (N) and Quaternary (Q). The strata have been continuously deposited since the Permian (P), with a depositional thickness generally exceeding 10 km and reaching a maximum of 15 km. The main lithologies are sandstone, siltstone, and mudstone, interbedded with tuff, oil shale, coal seams, etc.

[0124] In this embodiment, given that the study area is characterized by the widespread development of brittle strata interspersed with plastic strata, and is a heterogeneous distribution of brittle-plastic strata with detachment layers, this experiment strictly followed the principle of similarity. Two colors of quartz sand with identical mechanical properties, white and blue, and a particle size of 0.2–0.6 mm (internal friction angle of approximately 30°) were selected as aggregates to simulate dry rock strata. The mechanical properties of the sand conformed to the Coulomb-Mohr fracture criterion, and silica gel was used to simulate detachment layers.

[0125] In this process, the quartz sand needs to be treated accordingly. Adding different proportions of mud to the quartz sand can effectively simulate brittle-plastic strata.

[0126] In this embodiment, the designed sand-to-mud ratio range is 10:9 to 20:7. The upper and lower limits of the ratio are a continuous range, which can be gradually optimized based on experimental results.

[0127] In setting up experimental conditions, since the external force application speed, force application direction, compression (tension) distance and stratum thickness are also important factors affecting geological structure deformation, it is necessary to select multi-level experimental conditions that are closest to the real geological phenomena based on the actual structural deformation conditions in order to explore the best experimental scheme.

[0128] In this embodiment, in order to study the influence of different compression rates on the formation and evolution of the western piedmont fault-fold belt on the southern margin, the experiment adopted unilateral compression, with the moving end (left end) advancing to the right at a specific rate. Considering the heterogeneity of geological stress intensity and the compression rate calculated in the previous equilibrium evolution profile, the compression rate was designed to be in the range of 0.02 mm / s to 0.2 mm / s.

[0129] To investigate the influence of stratigraphic thickness on the formation and evolution of the western piedmont fault-fold belt on the southern margin, white quartz sand mixed with varying proportions of mud and water was used as the experimental material. Blue quartz sand was used as the stratigraphic boundary. The stratigraphic thickness was set to 4–11 cm, with a thickness similarity ratio between 4 × 10⁻⁶. -6 ~1.1×10 -5 .

[0130] In step S3, the conditional parameters that affect the results of the physical simulation experiment are determined as experimental factors, and the experimental level corresponding to each experimental factor is determined.

[0131] In this embodiment, 10:9, 5:4, 10:7, 5:3, 2:1, 20:9, 5:2, and 20:7 are taken from the designed sand-mud ratio interval to explore the influence of material ratio on the experimental results;

[0132] In this embodiment, 4 levels of extrusion rate are set, i.e., 0.02 mm / s, 0.05 mm / s, 0.1 mm / s, and 0.2 mm / s, to explore the influence of extrusion rate on the experimental results;

[0133] In this embodiment, 8 levels of extrusion distance are designed to explore the deformation of the geological model as much as possible under the premise of considering the size and feasibility of the experimental sandbox, i.e., 10 cm, 11 cm, 12 cm, 13 cm, 14 cm, 15 cm, 16 cm, and 17 cm.

[0134] In this embodiment, 4 cm, 5 cm, 6 cm, 7 cm, 8 cm, 9 cm, and 10 cm are taken from the designed stratum thickness interval to explore the influence of stratum thickness on the experimental results, and the silica gel slip layer is uniformly laid in the second layer.

[0135] In this embodiment, it should be noted that the values of the set extrusion rate, extrusion distance, and laid stratum thickness are representative values, and there is a continuous interval between the upper and lower limits. The uniform test design can be used to select and optimize the values according to the feedback of the experimental results. The entire experimental process does not consider sedimentation and erosion.

[0136] In step S4, the determined test factors and corresponding test levels are specifically designed to construct a factor level table.

[0137] In this embodiment, the experimental material selection and condition setting determine the test of 4 factors, i.e., extrusion distance, sand-mud ratio, extrusion rate, and simulated stratum thickness. The extrusion distance has 8 levels, and the other 3 factors each have 4 levels, which belongs to a mixed level test. The 3 factors need to be processed by quasi-level. The experimental specific settings are shown in Table 1.

[0138] Table 1: Factor level table of test design

[0139]

[0140] In step S5, the uniform design table is constructed according to the factor level table to design the test. The deviation D is used to measure the uniformity for the mixed level test, and the optimal uniform design table is constructed.

[0141] In this step, U * 8(8 5The uniform design table is used to design the experiment, and the uniformity is determined according to the U * 8(8 5 The columns 1, 2, 3 and 5 in the table are selected according to the regulation of the table, and the U * 8(8 4 The experiment is performed according to the table (table 2).

[0142] Table 2: U*8(8 4 The uniform design table

[0143]

[0144] Since the mixed level experiment can also be arranged in the U8(8×4 3 ) mixed level design table after the quasi-level processing, the uniformity of the two design tables is different, and the optimal design table needs to be further determined.

[0145] Here, the uniformity is measured by the deviation D for the mixed level experiment, and the combination of the columns x1, x2, x3 and x4 in table 2 can be calculated by the deviation D formula:

[0146]

[0147] Where: x1, x2, …, x n are n test points uniformly distributed in C m , x=(x1, x2, …, x m )∈Cm is any vector, n x is the number of points falling into [0, x] in x1, x2, …, x m , and n x / n represents the proportion of points falling in the rectangle [0, x], and v(x)=x1, x2, …, x m is the volume of the rectangle [0, x].

[0148] The deviation D of the mixed level design table U8(8 3 ×4) is 0.2918, and the deviation value of the uniform design table U * 8(8 4 ) is 0.2709.

[0149] The smaller the value of the deviation D, the better the uniformity; therefore, U * 8(8 4 ) is the optimal design table of the experiment scheme.

[0150] In step S6, each test parameter is returned to the optimal design scheme table of the uniform experiment design;

[0151] Table 3: Optimal design scheme table

[0152]

[0153] In step S7, the geological structure physical simulation experiment is carried out according to the optimal test design scheme, and the discrete Fréchet distance and the fractal dimension are used as indexes for the experimental results, and the multi-scale space target similarity measurement method is used for quantitative evaluation;

[0154] In this step, the corresponding geological structure physical simulation experiment is carried out according to the optimal design scheme table (table 3), and the experimental results are quantitatively evaluated. The multi-scale space line target similarity measurement method in the spatial relationship similarity theory is selected as the evaluation index, and the similarity of the profile fault is evaluated. Here, the change of the fold shape is not considered.

[0155] The discrete Fréchet distance between line targets is used as the position similarity of similarity measurement; wherein the discrete Fréchet distance calculation formula is:

[0156]

[0157] Wherein, d E (L 1，n , L 2，m ) is the Euclidean distance between two points L 1，n and L 2，m . Starting from (<L 1，n >, <L 2，m >), <L 1，1 , L 1，2 , …, L 1，n-1 > and <L 2，1 , L 2，2 , …, L 2，m-1 > can be regarded as line strings, and the discrete Fréchet distance between <L 1，1 , L 1，2 , …, L 1，n-1 > and <L 2，1 , L 2，2 , …, L 2，m-1 > is calculated recursively. When the line string is finally reduced to the starting point <L 1，1 >, <L 2，1 >), the calculation is terminated, and at this time the discrete Fréchet distance D Fd (<L 1，n >, <L 2，m >) = d E (L 1，1 , L 2，1 ).

[0158] The position similarity calculation formula based on the discrete Fréchet distance measurement is:

[0159]

[0160] where A and B are two spatial line targets, D Fd (A, B) is the discrete Fréchet distance between A and B, U is the maximum distance between any two points in A and B.

[0161] Shape similarity based on the fractal dimension of line targets;

[0162] Here, the fractal dimension in fractal theory has the characteristics of rotation, translation and scale invariance in shape description, which is a good shape measurement method, and can also take into account the local structure and overall distribution characteristics of spatial targets.

[0163] There are many kinds of fractal dimensions, among which the box-counting dimension is convenient to use grid covering method and double logarithmic coordinate fitting to obtain; therefore, the box-counting dimension in fractal dimension is used as the shape similarity measurement index, and the calculation formula of the box-counting dimension is:

[0164]

[0165] where r is the side length of the square grid covering the geometric trace, Nr is the number of non-empty grids intersecting the geometric trace, and D is the box-counting dimension value.

[0166] The shape similarity calculation formula based on the box-counting dimension measurement is:

[0167]

[0168] where A and B are two spatial line targets to be compared, SimFrc(A, B) is the shape similarity of spatial targets A and B, Dim(A) is the box-counting dimension of A, and Dim(B) is the box-counting dimension of B.

[0169] Here, the weights of each similarity are balanced, and the total similarity is calculated as the final evaluation index of the experiment; the total similarity calculation formula based on the multi-scale spatial line target similarity measurement is:

[0170] Sim Tot (A,B)=αSim d (A,B)+βSim Frc (A,B)

[0171] where Sim Tot (A, B) is the overall similarity of spatial line targets A and B, and α and β represent the weights of position relationship and shape relationship similarity respectively, here α and β take the same weight 1 / 2, and the experimental evaluation results are shown in Table 4.

[0172] Table 4 Experimental evaluation results

[0173]

[0174] In step S8, the experimental evaluation results are analyzed, a suitable mathematical model is constructed, and the significance of the test factors is tested by using the multiple regression method combined with F test and correlation coefficient (r) test, to determine the optimal test factor level parameters of the geological structure physical simulation experiment.

[0175] This step analyzes the evaluation results of the geological structure physical simulation experiment, determines the linear and nonlinear relationships, constructs a suitable mathematical model to represent the relationship between the factors and the evaluation results, and thus determines the optimal test factor level parameters of the geological structure physical simulation experiment. The F test calculation formula is:

[0176]

[0177] where i is the test factor serial number; n represents the number of test levels; yi n represents the test score index of the ith test factor at the corresponding level; represents the sum of the test score index of the ith test factor at the corresponding level.

[0178] Here, from Table 4, it can be seen that the best similarity is the 6th test, and the position similarity Sim d (A, B) is 87.63%, the shape similarity Sim Frc (A, B) is 90.19%, and the total similarity Sim Tot (A, B) is 88.91%.

[0179] The IBM SPSS Statistics software is used to analyze the scatter plot of the total similarity and each factor, and the scatter plots of the total similarity and the extrusion distance and the total similarity and the stratum thickness have obvious correlation (see Figure 5a and Figure 5b ), and there is a maximum value, so the extrusion distance and the stratum thickness are the most important factors affecting the experimental results.

[0180] The scatter plot of the total similarity and the sand-mud ratio (see Figure 5c ) has no regularity and obvious mutation, indicating that the sand-mud ratio has little effect on the experimental results, and the interaction between other factors will affect the experimental results.

[0181] The scatter plot of the total similarity and the extrusion rate after the quasi-horizontal (see Figure 5d ) also has no regularity and the extrusion rate at the same level has a large difference in the total similarity, so the single-factor extrusion rate also has little effect on the experiment, and the interaction between other factors will affect the experiment.

[0182] Since the total similarity has obvious nonlinear relationship with the extrusion distance and the stratum thickness, and is more in line with the characteristics of quadratic and cubic functions in the corresponding interval, the IBM SPSS Statistics software is used to perform quadratic and cubic function fitting on the total similarity, the extrusion distance and the stratum thickness (see Figure 6a and Figure 6b ).

[0183] Among them, the quadratic function relationship formula of the total similarity and the extrusion distance is Y 总 = -381.488 + 65.198x - 2.27x 2 , the determination coefficient is R 2 = 0.959, and F = 58.554 > F 0.05 (2, 5) = 5.79, and the correlation is significant.

[0184] The cubic function relationship formula of the total similarity and the extrusion distance is Y 总 = -251.46 + 35.174x - 0.056x 3 , the determination coefficient is R 2 = 0.964, and F = 66.469 > F 0.05 (2, 5) = 5.79, and the correlation is particularly significant.

[0185] Therefore, the cubic function model is more in line with the relationship between the total similarity and the extrusion distance.

[0186] The quadratic function relationship formula of the total similarity and the stratum thickness is Y 总 = -13.298 + 30.132x - 2.27x 2 , the determination coefficient is R 2 = 0.959, and F = 58.554 > F 0.05 (2, 5) = 5.79, and the correlation is significant.

[0187] The cubic function relationship formula of the total similarity and the extrusion distance is Y 总 = -62.274 + 52.293x - 5.396x 2 + 0.139x 3 , the determination coefficient is R 2 = 0.966, and F = 38.279 > F 0.05 (3, 4) = 6.59, and the correlation is significant; the cubic function model is more in line with the relationship between the total similarity and the stratum thickness.

[0188] The related parameters of the mathematical model of the total similarity, the extrusion distance and the extrusion rate are shown in Table 5.

[0189] Table 5: Mathematical model summary

[0190]

[0191] Here, the total similarity and the extrusion distance fitting cubic function and the total similarity and the strata thickness fitting cubic function are derived respectively, and the maximum value is obtained in the interval, when the extrusion distance and the strata thickness are 14.47 cm and 6.46 cm respectively, the total similarity reaches the maximum, as the optimal extrusion distance and strata thickness of the experiment.

[0192] The influence factor sand mud ratio and extrusion rate although the influence on the experimental results is very small and has no regularity, but it is an essential part of the experiment, and the interaction between other factors has an important influence on the experimental results, here the optimal sand mud ratio (10:7) and extrusion rate (0.1 mm / s) in 8 groups of experiments are taken to verify the best experiment.

[0193] In step S9, the geological structure physical simulation experiment verification and process analysis are carried out according to the optimal test factor level parameters.

[0194] In this step, the optimal experimental parameters are obtained according to the uniform test design and the corresponding curve regression analysis, that is, the extrusion distance is 14.47 cm, the strata thickness is 6.46 cm, the sand mud ratio is 10:7 and the extrusion rate is 0.1 mm / s, the structure physical simulation experiment is carried out according to the optimal experimental parameters, and the experimental results are shown in Figure 7 .

[0195] Among them, when the extrusion is 1.8 cm, a thrust fold with small uplift amplitude appears near the extrusion end, the thrust fault F1 is observed on the west profile, and the thrust faults F1 and F2 are observed on the east profile (as shown in Figure 7 ).

[0196] When the extrusion distance reaches 3.7 cm, small amplitude folds appear on both sides, and a new thrust fault is added, and the faults on the plane are nearly parallel;

[0197] When the extrusion distance reaches 6.5 cm, the front spreading thrust nappe structure is developed, the uplift amplitude of the two groups of folds is larger, the fault F1 near the extrusion end on the west profile develops branch fault (F1'), forming the recoil fault style, the fourth thrust fault (F3') begins to develop near the extrusion end on the east profile, the first and second rows of fault folds are basically formed, representing the tectonic movement of Yanshan period.

[0198] When the extrusion distance is 8.6 cm, the uplift amplitude of the thrust fold increases, the dip angle of the thrust fault increases, and the distance between the faults on the plane increases.

[0199] When the extrusion distance reaches 10.8 cm, the thrust fold uplift amplitude continues to increase, and is significantly affected by the decollement layer, a decollement fault (F4) extending far away from the extrusion end develops on the west profile, and a branch fault develops on the west profile to form a thrust structure, but does not penetrate the entire stratum, and is not shown on the plane, and the third row of structural belts is preliminarily uplifted.

[0200] When the extrusion distance reaches 12.2 cm, the decollement fault F4 continues to develop branch faults F6' on the west profile, and does not extend to the surface, and is not shown on the plane, and the decollement faults F5 and F6 develop on both sides of the decollement fault F4 on the east profile, and the F6 does not extend to the surface, and the branch fault F6' develops.

[0201] When the extrusion distance reaches 14.2 cm, a clear three-row fault-fold belt is formed, in which the first row and the second row are mainly fault-propagation folds with slow back wings and steep front wings, accompanied by stratum inversion, and the third row is greatly affected by the decollement layer, with a long displacement, and the stratum has not yet been inverted.

[0202] Here, 6 main faults and corresponding branch faults develop during the experiment, and the hanging wall of the faults F1 and F4 develops a recoil fault; this has a good correspondence with the three-row structural belt in the west-central segment of the western part of the southern margin of the Junggar Basin in the actual geological phenomenon; the phenomenon that the fault propagates forward in the decollement layer and bursts out of the surface also has a strong similarity with the Huomatu fault, and the structural difference between the upper and lower decollement layers is also restored through structural physical modeling.

[0203] Through uniform experimental design and mathematical model analysis, the optimal experimental parameters are obtained, and the structural physical modeling experiment is carried out with pertinence, and the experimental results have a strong similarity with the actual geological phenomenon, and the formation and evolution process of the foreland fault-fold belt in the western part of the southern margin of the Junggar Basin is successfully reproduced.

[0204] The experimental results can be used to illustrate the strong south-north horizontal extrusion in the Xishan period, in addition to intensifying the second row of anticline belts to become the first nappe, more importantly, the second and third rows of thrust-fold belts are formed with the Huoma, and the Tumantum anticline belt and the Dushanzi, Anjihai anticline belt as the front.

[0205] In addition, the decollement layer helps to some extent to propagate the stress, so that the structural belt develops in a more distant area from the orogenic belt, and also absorbs the tectonic stress, indirectly leading to the formation of recoil faults.

[0206] The thick-skin structure of the first row of anticline belts on the profile, the thin-skin structure of the second and third rows of anticline belts, the formation process of recoil structures, thrust folds and forward-propagating folds are well reproduced.

[0207] The technical scheme of the present application is described in detail above in combination with the drawings. In view of the fact that in the prior art, the geological structure physical simulation experiment is still in the exploratory, tedious and approximate experimental stage, and there is the problem of blindness and randomness without effective optimal scheme selection, the present application proposes a geological structure physical simulation experiment optimal scheme selection method, applies the uniform experiment design method to the optimal parameter selection of the geological structure physical simulation experiment, carries out the structure physical simulation experiment in a targeted manner, uses the discrete Fréchet distance and the fractal dimension as indexes for the experimental results, uses the multi-scale space target similarity measurement method for quantitative evaluation, uses the multiple regression method in combination with the F test and the correlation coefficient (r) test to test the significance of the experimental factors, reduces the number of experiments through the optimization algorithm, simplifies the experimental process, and thus quickly and accurately determines the optimal parameters and the level combination, so that the above technical problems can be well solved.

[0208] The embodiments of the present application have been described above, and the above description is exemplary and is not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for selecting an optimized scheme for a physical simulation experiment of geological structures, characterized in that, include: S1: Determine the geological background, development, and geometric, kinematic, and dynamic characteristics of the simulated objects in the geological tectonic physics simulation experiment, and clarify the purpose and requirements of the tectonic physics simulation experiment; S2: Based on the purpose of the geological structure physical simulation experiment, define the geological model, and set up the experimental apparatus and model, select experimental materials, and set up experimental conditions. S3: Determine the conditional parameters that affect the results of the structural physics simulation experiment as experimental factors, and determine the experimental level corresponding to each experimental factor; S4: Design specific experimental factors and corresponding experimental levels, and construct a factor level table; S5: Construct a uniform design table based on the factor level table to design the experiment. For mixed-level experiments, use the deviation D to measure uniformity and construct the optimal uniform design table. S6: Based on the optimal uniform design table, reset each experimental parameter and translate it into the optimal design scheme table of uniform experimental design; S7: Based on the optimal experimental design scheme table, conduct corresponding geological structure physical simulation experiments. Use discrete Fréchet distance and fractal dimension as indicators for the experimental results, and use multi-scale spatial target similarity measurement method for quantitative evaluation. S8: Analyze the quantitative evaluation results of the experiment, construct a suitable mathematical model, and use the multiple regression method combined with the F test and the correlation coefficient (r) test to test the significance of the experimental factors and clarify the optimal experimental factor level parameters for the geological structure physical simulation experiment. S9: Conduct geological structural physical simulation experiments and process analysis based on the optimal experimental factor level parameters.

2. The method for selecting an optimized scheme for a physical simulation experiment of geological structures according to claim 1, characterized in that, S1 specifically includes: The geological tectonic background of the target work area should be investigated, the geometric characteristics, kinematic processes and dynamic background of the geological structure development in the work area should be clarified, the tectonic stress background of different geological periods should be determined as extension, compression or strike-slip, and the purpose and requirements of the tectonic physics simulation experiment should be clarified.

3. The method for selecting an optimized scheme for geological structural physical simulation experiments according to claim 1, characterized in that, S2 specifically includes: Based on the purpose of the geological structure physics simulation experiment, the geological model to be used for the physics simulation experiment is defined, including the geological profile interpreted from the seismic profile. A fully automated geological structure physical simulation experimental sandbox system was used as the experimental device. The specifications of the experimental model sandbox were set according to the size of the target work area and the similarity ratio. The driving units at both ends of the experimental model sandbox were set to tension or compression, fixed or movable, unidirectional or bidirectional according to the structural stress background and stress conditions of the work area. The boundary conditions of the experimental model sandbox were set according to the positional relationship of the actual geological phenomena in the work area. Based on the rock mechanical properties of the study area, the most similar experimental materials were selected, and based on the actual structural deformation conditions, the external factors that most closely resembled the real geological phenomena were selected for the experiment. The external factors included: the external force application rate, the force application direction, the compression or tension distance, and the stratum thickness.

4. The method for selecting an optimized scheme for geological structural physical simulation experiments according to claim 1, characterized in that, S5 specifically includes: Using the aforementioned factor level table, a uniform design table is constructed to design experiments. For mixed-level experiments, the uniformity is measured using the deviation D calculation formula to obtain the deviation D value of the mixed-level design table. The deviation D value of the mixed-level design table is compared with the deviation D value of the uniform design table, and the optimal uniform design table is constructed based on the design table with the smallest deviation D value.

5. The method for selecting an optimized scheme for geological structural physical simulation experiments according to claim 4, characterized in that, The formula for calculating the deviation D is: Where x1, x2, ..., x n For the vector set C m n test points are uniformly distributed in the middle, x = (x1, x2, ..., xn). m )∈C m Let n be any vector x Let x1, x2, ..., x m The number of points that fall within the range [0, x], n x / n represents the proportion of points falling within the rectangle [0, x], ν(x) = x1, x2, ..., x m Let be the volume of the rectangle [0, x], D denote the deviation, and sup denote the supremum of the set.

6. The method for selecting an optimized scheme for geological structural physical simulation experiments according to claim 1, characterized in that, Specifically, S7 includes: Conduct corresponding geological structure physical simulation experiments according to the optimal design scheme table; The experimental results were quantitatively evaluated. The evaluation index adopted was the multi-scale spatial line target similarity measurement method based on spatial relationship similarity theory, with the discrete Fréchet distance between line targets as the measurement index of positional similarity. Shape similarity is measured by the fractal dimension of the line target, and the box-counting dimension in the fractal dimension is used as the metric for shape similarity. The weights of positional similarity and shape similarity are balanced, and the total similarity is calculated as the final evaluation index of the experiment.

7. The method for selecting an optimized scheme for geological structural physical simulation experiments according to claim 6, characterized in that, The formula for calculating the discrete Fréchet distance is: Where, d E (L 1，n L 2，m () are two points L 1，n With L 2，m The Euclidean distance between them, from (<L) 1，n >, <L 2，m >) start, you can add <L 1，1 L 1，2 , ..., L 1，n-1 > and <L 2，1 L 2，2 , ..., L 2，m-1 > can be considered as a string of lines, and <L can be calculated recursively. 1，1 L 1，2 , ..., L 1，n-1 > and <L 2，1 L 2，2 , ..., L 2，m-1 The discrete Fréchet distance between > is calculated when the line string eventually decreases to the starting point <L. 1，1 >, <L 2，1 When >), the calculation terminates, at which point the discrete Fréchet distance D is reached. Fd (<L) 1，n >, <L 2，m >) = d E (L 1，1 L 2，1 ); The formula for calculating the positional similarity is: Where A and B are two spatial line targets to be compared, and D... Fd (A, B) represents the discrete Fréchet distance between A and B, U is the maximum distance between any two points in targets A and B, and Sim d (A,B) represents the positional similarity between A and B.

8. The method for selecting an optimized experimental scheme for geological structural physical simulation according to claim 7, characterized in that, The formula for calculating the box dimension is: Where r is the side length of the square grid covering the geometric pattern, Nr is the number of non-empty grids intersecting with the geometric pattern, and D is the box dimension value; The formula for calculating the shape similarity is: Where A and B are two spatial line targets to be compared, SimFrc(A, B) is the shape similarity between spatial line targets A and B, Dim(A) is the box-count dimension of A, Dim(B) is the box-count dimension of B, and Sim... Frc (A,B) represents the shape similarity between A and B.

9. The method for selecting an optimized scheme for a physical simulation experiment of geological structures according to claim 8, characterized in that, The formula for calculating the total similarity is: Sim Tot (A,B)=αSim d (A,B)+βSim Frc (A,B) Among them, Sim Tot (A, B) represents the overall similarity between spatial line targets A and B, where α and β represent the weights of positional and shape similarity, respectively.

10. The method for selecting an optimized experimental scheme for geological structural physical simulation according to claim 1, characterized in that, S8 specifically includes: The evaluation results of the physical simulation experiment of geological structure are analyzed to clarify the linear and nonlinear relationships, and a corresponding mathematical model is constructed to characterize the relationship between the factors and the evaluation results, thereby clarifying the optimal experimental factor level parameters for the physical simulation experiment of geological structure. The formula for calculating the F-test is: Where i is the experimental factor sequence number, n represents the number of experimental level replicates, and yi n This represents the test score index for the i-th test factor at the corresponding level. This represents the summation of the test score indicators for the i-th test factor at the corresponding level.

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